基于信任计算和矩阵分解的推荐算法

王瑞琴1潘俊2冯建军1

摘要基于矩阵分解的推荐算法普遍存在数据稀疏性二冷启动和抗攻击能力差等问题.针对上述问题,文中提出信任加强的矩阵分解推荐算法.首先,借鉴社会心理学中的信任产生原理,提出基于用户信誉度的信任扩展方法,缓解信任数据的稀疏性问题.然后,基于社交同质化原理,利用信任用户对评分矩阵分解过程中的用户潜在因子向量进行扩展,解决评分数据的稀疏性和新用户的冷启动问题.同时,利用信任关系对目标优化函数进行规格化约束,提高评分预测的准确性.基于通用测试数据集Epinions的实验表明,文中方法在推荐性能方面具有明显改善,可以有效解决数据稀疏性问题和冷启动问题.

关键词社交信任,信誉度,信任传递,矩阵分解,规格化

引用格式王瑞琴,潘俊,冯建军.基于信任计算和矩阵分解的推荐算法.模式识别与人工智能,2018, 31(9):786-796.

DOI10.16451/https://www.360docs.net/doc/184213646.html,ki.issn1003-6059.201809002 中图法分类号TP3;TP181

Recommendation Algorithm

Based on Trust Computation and Matrix Factorization

WANG Ruiqin1,PAN Jun2,FENG Jianjun1

ABSTRACT The recommendation algorithm based on matrix factorization has problems of data sparsity,cold start,poor anti-attack ability,etc.Therefore,a trust-based matrix factorization recommendation algorithm is proposed.Firstly,based on the principle of trust generation in social psychology,a reputation-based trust computation method is proposed to alleviate the trust data sparsity problem.Then,grounded on the principle of social homogenization,the user latent factor vector in the process of matrix factorization is extended by using the trust users to solve the rating data sparsity and new-user cold start problem.Meanwhile,social trust relationships are utilized to normalize the target function to improve the accuracy of the rating prediction.Experimental results on Epinions dataset show that the proposed method improves the recommendation precision greatly compared with the state-of-the-art methods,and it effectively solves the problems of data sparsity and cold start.

Key Words Social Trust,Reputation,Trust Propagation,Matrix Factorization,Regularization Citation WANG R Q,PAN J,FENG J J.Recommendation Algorithm Based on Trust Computation and Matrix Factorization.Pattern Recognition and Artificial Intelligence,2018,31(9):786-796.

收稿日期:2018-05-22;录用日期:2018-08-20 Manuscript received May22,2018;

accepted August20,2018

浙江省科技计划重点研发项目(No.2017C03047)资助Supported by Key Research and Development Project for Science and Technology Program of Zhejiang Province(No.2017C03047)本文责任编委林鸿飞Recommended by Associate Editor LIN Hongfei

1.湖州师范学院信息工程学院湖州313000

2.温州大学商业建模与数据挖掘研究所温州325035 1.School of Information Engineering,Huzhou University,

在信息爆炸的当代,推荐系统作为一种有效的信息过滤技术,广泛使用在各种电子商务平台和社交网络中,如Amazon二eBay二Netflix二Epinions二Flixster 和Lastfm等.自从2009年Netflix Prize百万美金竞赛以来,基于矩阵分解(Matrix Factorization)的协同推荐技术以其在大赛中的优异表现得到业界的广泛关注.矩阵分解的核心思想是用户的兴趣只受少数Huzhou313000

2.Institute of Business Modeling and Data Mining,Wenzhou U-niversity,Wenzhou325035

第31卷第9期模式识别与人工智能Vol.31 No.9 2018年9月Pattern Recognition and Artificial Intelligence Sep.2018

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